import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY





@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class     lowerCAmelCase__     (				unittest.TestCase			):


		'''simple docstring'''






		lowerCAmelCase_     =   MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
		def   _snake_case       (       self     :     Dict     ,  snake_case__     :     Dict     ,  snake_case__     :     Any     ,  snake_case__     :     int		)      ->	Optional[Any]:
				_lowerCamelCase				   =  hf_hub_download(
				    repo_id='nateraw/video-demo'     ,  filename='archery.mp4'     ,  repo_type='dataset'		)
				_lowerCamelCase				   =  VideoClassificationPipeline(model=snake_case__     ,  image_processor=snake_case__     ,  top_k=2		)
				_lowerCamelCase				   =  [
				    example_video_filepath,
				    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
				]
				return video_classifier, examples
		def   _snake_case       (       self     :     List[str]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     Optional[int]		)      ->	int:
				for example in examples:
						_lowerCamelCase				   =  video_classifier(snake_case__		)

						self.assertEqual(
						    snake_case__     ,  [
						        {'score': ANY(snake_case__		), 'label': ANY(snake_case__		)},
						        {'score': ANY(snake_case__		), 'label': ANY(snake_case__		)},
						    ]     ,  )
		@require_torch
		def   _snake_case       (       self     :     List[Any]		)      ->	Union[str, Any]:
				_lowerCamelCase				   =  'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
				_lowerCamelCase				   =  VideoMAEFeatureExtractor(
				    size={'shortest_edge': 1_0}     ,  crop_size={'height': 1_0, 'width': 1_0}		)
				_lowerCamelCase				   =  pipeline(
				    'video-classification'     ,  model=snake_case__     ,  feature_extractor=snake_case__     ,  frame_sampling_rate=4		)

				_lowerCamelCase				   =  hf_hub_download(repo_id='nateraw/video-demo'     ,  filename='archery.mp4'     ,  repo_type='dataset'		)
				_lowerCamelCase				   =  video_classifier(snake_case__     ,  top_k=2		)
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [{'score': 0.5199, 'label': 'LABEL_0'}, {'score': 0.4801, 'label': 'LABEL_1'}]     ,  )

				_lowerCamelCase				   =  video_classifier(
				    [
				        video_file_path,
				        video_file_path,
				    ]     ,  top_k=2     ,  )
				self.assertEqual(
				    nested_simplify(snake_case__     ,  decimals=4		)     ,  [
				        [{'score': 0.5199, 'label': 'LABEL_0'}, {'score': 0.4801, 'label': 'LABEL_1'}],
				        [{'score': 0.5199, 'label': 'LABEL_0'}, {'score': 0.4801, 'label': 'LABEL_1'}],
				    ]     ,  )



		@require_tf
		def   _snake_case       (       self     :     Optional[int]		)      ->	Union[str, Any]:
				pass
def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)
		binary_number += "0" * shift_amount
		return binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number < 0 or shift_amount < 0:
				raise ValueError('both inputs must be positive integers'							)

		_lowerCamelCase				   =  str(bin(UpperCamelCase							)							)[2:]
		if shift_amount >= len(UpperCamelCase							):
				return "0b0"
		_lowerCamelCase				   =  binary_number[: len(UpperCamelCase							) - shift_amount]
		return "0b" + shifted_binary_number


def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    str:
		if number >= 0:  # Get binary representation of positive number
				_lowerCamelCase				   =  '0' + str(bin(UpperCamelCase							)							).strip('-'							)[2:]
		else:  # Get binary (2's complement) representation of negative number
				_lowerCamelCase				   =  len(bin(UpperCamelCase							)[3:]							)  # Find 2's complement of number
				_lowerCamelCase				   =  bin(abs(UpperCamelCase							) - (1 << binary_number_length)							)[3:]
				_lowerCamelCase				   =  (
				    '1' + '0' * (binary_number_length - len(UpperCamelCase							)) + binary_number
				)

		if shift_amount >= len(UpperCamelCase							):
				return "0b" + binary_number[0] * len(UpperCamelCase							)
		return (
		    "0b"
		    + binary_number[0] * shift_amount
		    + binary_number[: len(UpperCamelCase							) - shift_amount]
		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()